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Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. [22] Effective visualization helps users analyze and reason about data and evidence. [23] It makes complex data more accessible, understandable, and usable, but can also be reductive. [24]
Numeric literals in Python are of the normal sort, e.g. 0, -1, 3.4, 3.5e-8. Python has arbitrary-length integers and automatically increases their storage size as necessary. Prior to Python 3, there were two kinds of integral numbers: traditional fixed size integers and "long" integers of arbitrary size.
DOT is a graph description language, developed as a part of the Graphviz project. DOT graphs are typically stored as files with the .gv or .dot filename extension — .gv is preferred, to avoid confusion with the .dot extension used by versions of Microsoft Word before 2007.
In California, highway lanes may be marked either solely by Botts' dots, or dots placed over painted lines. Four dots are used for broken lines on freeways, and broken lines on surface streets may use only three dots. Reflective pavement markers are placed at regular intervals between Botts' dots to increase the visibility of lane markings at night
The algorithm for computing a dot plot is closely related to kernel density estimation. The size chosen for the dots affects the appearance of the plot. Choice of dot size is equivalent to choosing the bandwidth for a kernel density estimate. In the R programming language this type of plot is also referred to as a stripchart [3] or stripplot. [4]
Keeping data hidden helps prevent problems when changing the code later. [49] Some programming languages, like Java, control information hiding by marking variables as private (hidden) or public (accessible). [50] Other languages, like Python, rely on naming conventions, such as starting a private method's name with an underscore.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.